get_default_model_options: Get default model options

In MOFA2: Multi-Omics Factor Analysis v2

Description

Function to obtain the default model options.

Usage

get_default_model_options(object)

Arguments

object

an untrained MOFA object

Details

This function provides a default set of model options that can be modified and passed to the MOFA object in the prepare_mofa step (see example), i.e. after creating a MOFA object (using create_mofa) and before starting the training (using run_mofa) The model options are the following:

• likelihoods: character vector with data likelihoods per view: 'gaussian' for continuous data (Default for all views), 'bernoulli' for binary data and 'poisson' for count data.

• num_factors: numeric value indicating the (initial) number of factors. Default is 15.

• spikeslab_factors: logical indicating whether to use spike and slab sparsity on the factors (Default is FALSE)

• spikeslab_weights: logical indicating whether to use spike and slab sparsity on the weights (Default is TRUE)

• ard_factors: logical indicating whether to use ARD sparsity on the factors (Default is TRUE only if using multiple groups)

• ard_weights: logical indicating whether to use ARD sparsity on the weights (Default is TRUE)

Value

Returns a list with the default model options.

Examples

# Using an existing simulated data with two groups and two views
file <- system.file("extdata", "test_data.RData", package = "MOFA2")

# Load data dt (in data.frame format)
load(file) 

# Create the MOFA object
MOFAmodel <- create_mofa(dt)

# Load default model options
model_opts <- get_default_model_options(MOFAmodel)

# Edit some of the model options
model_opts$num_factors <- 10
model_opts$spikeslab_weights <- FALSE

# Prepare the MOFA object
MOFAmodel <- prepare_mofa(MOFAmodel, model_options = model_opts)

MOFA2 documentation built on Nov. 8, 2020, 7:28 p.m.